Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta

Introduction

In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult.

Methods

By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost.

Results

Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.

An extendable optical fibre probe survey meter for naturally occurring radioactive material (NORM) and other weak emitters

We have developed a radioluminescence-based survey meter for use in industries in which there is involvement in naturally occurring radioactive material (NORM), also in support of those needing to detect other weak emitters of radiation. The functionality of the system confronts particular shortcomings of the handheld survey meters that are currently being made use of. The device couples a LYSO:Ce scintillator with a photodetector via a polymer optical fibre waveguide, allowing for "intrinsically safe" inspection within pipework, separators, valves and other such component pieces. The small-diameter optical fibre probe is electrically passive, immune to electromagnetic interference, and chemically inert. The readout circuit is entirely incorporated within a handheld casing housing a silicon photomultiplier (SiPM) detection circuit and a microprocessor circuit connected to an LCD display. A 15 m long flexible PMMA optical fibre waveguide is butt coupled to an ABS plastic probe that retains the LYSO:Ce scintillator. Initial tests have included the use of lab-based mixed gamma-ray sources, measurements being made in concert with a reference conventional GM survey-meter. Characterization, via NORM sources at a decontamination facility, has shown useful sensitivity, covering the dose-rate range 0.10- to 28 µSv h^-1 (R-squared 0.966), extending to 80 µSv/h as demonstrated in use of a Cs-137 source. The system is shown to provide an effective tool for detection of radioactivity within hard to access locations, in particular for sources emitting at low radiation levels, down to values that approach background.

Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence

Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18–25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.

Genotypic characterization of Echinococcus granulosus isolates based on the mitochondrial cytochrome c oxidase 1 (cox1) gene in Northwest Iran

Hydatidosis is one of the most important zoonotic parasitic diseases caused by the larval stage of Echinococcus granulosus which causes great health and economic losses. The aim of this study was to use the sequencing method to evaluate genotypes of E. granulosus isolated from humans and bovines using mitochondrial cytochrome c oxidase subunit 1 (cox1) gene. The samples were taken in the East Azerbaijan Province, Northwest Iran. Overall, 26 hydatid cyst samples (10 human and 16 cattle isolates) were collected. DNA extraction was taken from the protoscoleces of human and germinal layer of bovine samples. PCR was performed using the mitochondrial cytochrome c oxidase subunit 1(cox1) gene, and then it was sequenced. Sequences were analyzed for identification of their genotypes. All 16 bovine isolates were recognized as G1 genotypes (sheep strain) and G1B subtypes. Out of ten human host samples, seven isolates were G1B subtypes, and three samples were identified as G3 genotypes. The results of this study showed that G1 and especially G1B are the predominant genotype and subtype in humans and cattle in Northwest Iran.